scholarly journals SwiftOrtho: A fast, memory-efficient, multiple genome orthology classifier

GigaScience ◽  
2019 ◽  
Vol 8 (10) ◽  
Author(s):  
Xiao Hu ◽  
Iddo Friedberg
Keyword(s):  
Protein Function ◽  
Large Scale ◽  
Homology Search ◽  
Comparative Genomic ◽  
Data Sets ◽  
Analysis Tool ◽  
Memory Usage ◽  
Spaced Seeds ◽  
Speed Up ◽  
Genome Analyses

Abstract Background Gene homology type classification is required for many types of genome analyses, including comparative genomics, phylogenetics, and protein function annotation. Consequently, a large variety of tools have been developed to perform homology classification across genomes of different species. However, when applied to large genomic data sets, these tools require high memory and CPU usage, typically available only in computational clusters. Findings Here we present a new graph-based orthology analysis tool, SwiftOrtho, which is optimized for speed and memory usage when applied to large-scale data. SwiftOrtho uses long k-mers to speed up homology search, while using a reduced amino acid alphabet and spaced seeds to compensate for the loss of sensitivity due to long k-mers. In addition, it uses an affinity propagation algorithm to reduce the memory usage when clustering large-scale orthology relationships into orthologous groups. In our tests, SwiftOrtho was the only tool that completed orthology analysis of proteins from 1,760 bacterial genomes on a computer with only 4 GB RAM. Using various standard orthology data sets, we also show that SwiftOrtho has a high accuracy. Conclusions SwiftOrtho enables the accurate comparative genomic analyses of thousands of genomes using low-memory computers. SwiftOrtho is available at https://github.com/Rinoahu/SwiftOrtho

scholarly journals SwiftOrtho: a Fast, Memory-Efficient, Multiple Genome Orthology Classifier

2019 ◽  
Author(s):  
Xiao Hu ◽  
Iddo Friedberg
Keyword(s):  
Protein Function ◽  
Large Scale ◽  
Comparative Genomic ◽  
Analysis Tool ◽  
Bacterial Genomes ◽  
Function Annotation ◽  
Large Scale Data ◽  
Protein Function Annotation ◽  
Genome Analyses ◽  
Memory Efficient

AbstractIntroductionGene homology type classification is a requisite for many types of genome analyses, including comparative genomics, phylogenetics, and protein function annotation. A large variety of tools have been developed to perform homology classification across genomes of different species. However, when applied to large genomic datasets, these tools require high memory and CPU usage, typically available only in costly computational clusters. To address this problem, we developed a new graph-based orthology analysis tool, SwiftOrtho, which is optimized for speed and memory usage when applied to large-scale data.ResultsIn our tests, SwiftOrtho is the only tool that completed orthology analysis of 1,760 bacterial genomes on a computer with only 4GB RAM. Using various standard orthology datasets, we also show that SwiftOrtho has a high accuracy. SwiftOrtho enables the accurate comparative genomic analyses of thousands of genomes using low memory computers.Availabilityhttps://github.com/Rinoahu/SwiftOrtho

scholarly journals A Universal Nonparametric Event Detection Framework for Neuropixels Data

2019 ◽  
Author(s):  
Hao Chen ◽  
Shizhe Chen ◽  
Xinyi Deng
Keyword(s):  
Change Point ◽  
Large Scale ◽  
Brain Regions ◽  
General Purpose ◽  
Electrophysiological Recording ◽  
Neural Population ◽  
Data Sets ◽  
Analysis Tool ◽  
Neural Activities ◽  
Spontaneous Behavior

SummaryNeuropixels probes present exciting new opportunities for neuroscience, but such large-scale high-density recordings also introduce unprecedented challenges in data analysis. Neuropixels data usually consist of hundreds or thousands of long stretches of sequential spiking activities that evolve non-stationarily over time and are often governed by complex, unknown dynamics. Extracting meaningful information from the Neuropixels recordings is a non-trial task. Here we introduce a general-purpose, graph-based statistical framework that, without imposing any parametric assumptions, detects points in time at which population spiking activity exhibits simultaneous changes as well as changes that only occur in a subset of the neural population, referred to as “change-points”. The sequence of change-point events can be interpreted as a footprint of neural population activities, which allows us to relate behavior to simultaneously recorded high-dimensional neural activities across multiple brain regions. We demonstrate the effectiveness of our method with an analysis of Neuropixels recordings during spontaneous behavior of an awake mouse in darkness. We observe that change-point dynamics in some brain regions display biologically interesting patterns that hint at functional pathways, as well as temporally-precise coordination with behavioral dynamics. We hypothesize that neural activities underlying spontaneous behavior, though distributed brainwide, show evidences for network modularity. Moreover, we envision the proposed framework to be a useful off-the-shelf analysis tool to the neuroscience community as new electrophysiological recording techniques continue to drive an explosive proliferation in the number and size of data sets.

scholarly journals Piggy: A Rapid, Large-Scale Pan-Genome Analysis Tool for Intergenic Regions in Bacteria

2017 ◽  
Author(s):  
Harry A. Thorpe ◽  
Sion C. Bayliss ◽  
Samuel K. Sheppard ◽  
Edward J. Feil
Keyword(s):  
Large Scale ◽  
Reference Database ◽  
Analysis Tool ◽  
Protein Coding ◽  
Coding Sequences ◽  
Large Genome ◽  
Pan Genome ◽  
Overwhelming Evidence ◽  
Intergenic Regions ◽  
Genome Analyses

AbstractDespite overwhelming evidence that variation in intergenic regions (IGRs) in bacteria impacts on phenotypes, most current approaches for analysing pan-genomes focus exclusively on protein-coding sequences. To address this we present Piggy, a novel pipeline that emulates Roary except that it is based only on IGRs. We demonstrate the use of Piggy for pan-genome analyses of Staphylococcus aureus and Escherichia coli using large genome datasets. For S. aureus, we show that highly divergent (“switched”) IGRs are associated with differences in gene expression, and we establish a multi-locus reference database of IGR alleles (igMLST; implemented in BIGSdb). Piggy is available at https://github.com/harry-thorpe/piggy.

scholarly journals Development of Large-scale Cross-linking Mass Spectrometry

2017 ◽  
Vol 17 (6) ◽  
pp. 1055-1066 ◽  
Author(s):  
Helena Maria Barysz ◽  
Johan Malmström
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Protein Function ◽  
Large Scale ◽  
Unmet Need ◽  
Large Data ◽  
Cross Linking ◽  
Data Sets ◽  
Protein Protein Interactions ◽  
Model Complex

Cross-linking mass spectrometry (CLMS) provides distance constraints to study the structure of proteins, multiprotein complexes and protein-protein interactions which are critical for the understanding of protein function. CLMS is an attractive technology to bridge the gap between high-resolution structural biology techniques and proteomic-based interactome studies. However, as outlined in this review there are still several bottlenecks associated with CLMS which limit its application on a proteome-wide level. Specifically, there is an unmet need for comprehensive software that can reliably identify cross-linked peptides from large data sets. In this review we provide supporting information to reason that targeted proteomics of cross-links may provide the required sensitivity to reliably detect and quantify cross-linked peptides and that a reporter ion signature for cross-linked peptides may become a useful approach to increase confidence in the identification process of cross-linked peptides. In addition, the review summarizes the recent advances in CLMS workflows using the analysis of condensin complex in intact chromosomes as a model complex.

scholarly journals Reanalysis of global proteomic and phosphoproteomic data identified a large number of glycopeptides

2017 ◽  
Author(s):  
Yingwei Hu ◽  
Punit Shah ◽  
David J. Clark ◽  
Minghui Ao ◽  
Hui Zhang
Keyword(s):  
Breast Cancer ◽  
Protein Function ◽  
Large Scale ◽  
Vital Role ◽  
Protein Glycosylation ◽  
Data Sets ◽  
Data Set ◽  
Breast Cancer Xenograft ◽  
Proteomic Data ◽  
Large Scale Analysis

ABSTRACTProtein glycosylation plays fundamental roles in many cellular processes, and previous reports have shown dysregulation to be associated with several human diseases, including diabetes, cancer, and neurodegenerative disorders. Despite the vital role of glycosylation for proper protein function, the analysis of glycoproteins has been lagged behind to other protein modifications. In this study, we describe the re-analysis of global proteomic data from breast cancer xenograft tissues using recently developed software package GPQuest 2.0, revealing a large number of previously unidentifiedN-linked glycopeptides. More importantly, we found that using immobilized metal affinity chromatography (IMAC) technology for the enrichment of phosphopeptides had co-enriched a substantial number of sialoglycopeptides, allowing for a large-scale analysis of sialoglycopeptides in conjunction with the analysis of phosphopeptides. Collectively, combined MS/MS analyses of global proteomic and phosphoproteomic datasets resulted in the identification of 6,724 N-linked glycopeptides from 617 glycoproteins derived from two breast cancer xenograft tissues. Next, we utilized GPQuest for the re-analysis of global and phosphoproteomic data generated from 108 human breast cancer tissues that were previously analyzed by Clinical Proteomic Analysis Consortium (CPTAC). Reanalysis of the CPTAC dataset resulted in the identification of 2,683 glycopeptides from the global proteomic data set and 4,554 glycopeptides from phosphoproteomic data set, respectively. Together, 11,292 N-linked glycopeptides corresponding to 1,731 N-linked glycosites from 883 human glycoproteins were identified from the two data sets. This analysis revealed an extensive number of glycopeptides hidden in the global and enriched in IMAC-based phosphopeptide-enriched proteomic data, information which would have remained unknown from the original study otherwise. The reanalysis described herein can be readily applied to identify glycopeptides from already existing data sets, providing insight into many important facets of protein glycosylation in different biological, physiological, and pathological processes.

LEVEL OF DETAIL AND MULTI-RESOLUTION MODELING FOR VIRTUAL PROTOTYPING

2001 ◽  
Vol 01 (02) ◽  
pp. 329-343 ◽  
Author(s):  
ZHIGENG PAN ◽  
MINGMIN ZHANG ◽  
KUN ZHOU ◽  
CHIYI CHENG ◽  
JIAOYING SHI
Keyword(s):  
Virtual Reality ◽  
Large Scale ◽  
Virtual Prototyping ◽  
Level Of Detail ◽  
Data Sets ◽  
Virtual Design ◽  
Resolution Model ◽  
Cad Models ◽  
Speed Up ◽  
Representation Scheme

Reconciling scene realism with interactivity has emerged as one of the most important areas in making virtual reality feasible for large-scale CAD data sets consisting of several millions of primitives. Level of detail (LoD) and multi-resolution modeling techniques in virtual reality can be used to speed up the process of virtual design and virtual prototyping. In this paper, we present an automatic LoD generation and rendering algorithm, which is suitable for CAD models and propose a new multi-resolution representation scheme called MRM (multi-resolution model), which can support efficient extraction of fixed resolution and variable resolution for multiple objects in the same scene. MRM scheme supports unified selective simplifications and selective refinements over the mesh. Furthermore, LoD and multi-resolution models may be used to support real-time geometric transmission in collaborative virtual design and prototyping.

scholarly journals Residue Geometry Networks: A Rigidity-Based Approach to the Amino Acid Network and Evolutionary Rate Analysis

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Alexander S. Fokas ◽  
Daniel J. Cole ◽  
Sebastian E. Ahnert ◽  
Alex W. Chin
Keyword(s):  
Amino Acid ◽  
Protein Function ◽  
Evolutionary Rate ◽  
Data Sets ◽  
Analysis Tool ◽  
Construction Technique ◽  
Strong Negative Correlation ◽  
Rate Analysis ◽  
Dynamics Simulations ◽  
Dynamical Function

Abstract Amino acid networks (AANs) abstract the protein structure by recording the amino acid contacts and can provide insight into protein function. Herein, we describe a novel AAN construction technique that employs the rigidity analysis tool, FIRST, to build the AAN, which we refer to as the residue geometry network (RGN). We show that this new construction can be combined with network theory methods to include the effects of allowed conformal motions and local chemical environments. Importantly, this is done without costly molecular dynamics simulations required by other AAN-related methods, which allows us to analyse large proteins and/or data sets. We have calculated the centrality of the residues belonging to 795 proteins. The results display a strong, negative correlation between residue centrality and the evolutionary rate. Furthermore, among residues with high closeness, those with low degree were particularly strongly conserved. Random walk simulations using the RGN were also successful in identifying allosteric residues in proteins involved in GPCR signalling. The dynamic function of these residues largely remain hidden in the traditional distance-cutoff construction technique. Despite being constructed from only the crystal structure, the results in this paper suggests that the RGN can identify residues that fulfil a dynamical function.

scholarly journals PRISMS-Fatigue computational framework for fatigue analysis in polycrystalline metals and alloys

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Mohammadreza Yaghoobi ◽  
Krzysztof S. Stopka ◽  
Aaditya Lakshmanan ◽  
Veera Sundararaghavan ◽  
John E. Allison ◽  
...  
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Large Scale ◽  
Metals And Alloys ◽  
Analysis Tool ◽  
Computational Framework ◽  
Crystal Plasticity Finite Element ◽  
Polycrystalline Metals ◽  
Simulation Based ◽  
Open Source Framework

AbstractThe PRISMS-Fatigue open-source framework for simulation-based analysis of microstructural influences on fatigue resistance for polycrystalline metals and alloys is presented here. The framework uses the crystal plasticity finite element method as its microstructure analysis tool and provides a highly efficient, scalable, flexible, and easy-to-use ICME community platform. The PRISMS-Fatigue framework is linked to different open-source software to instantiate microstructures, compute the material response, and assess fatigue indicator parameters. The performance of PRISMS-Fatigue is benchmarked against a similar framework implemented using ABAQUS. Results indicate that the multilevel parallelism scheme of PRISMS-Fatigue is more efficient and scalable than ABAQUS for large-scale fatigue simulations. The performance and flexibility of this framework is demonstrated with various examples that assess the driving force for fatigue crack formation of microstructures with different crystallographic textures, grain morphologies, and grain numbers, and under different multiaxial strain states, strain magnitudes, and boundary conditions.

scholarly journals Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

2020 ◽  
Vol 37 ◽  
Author(s):  
Lior Shamir
Keyword(s):  
Large Scale ◽  
Spiral Galaxies ◽  
Hubble Space Telescope ◽  
Gravitational Interaction ◽  
Large Data ◽  
Sloan Digital Sky Survey ◽  
Data Sets ◽  
Dipole Axis ◽  
Data Set ◽  
The Asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .

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